WO2023286171A1 - Dispositif de recherche, procédé de recherche et programme de recherche - Google Patents

Dispositif de recherche, procédé de recherche et programme de recherche Download PDF

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Publication number
WO2023286171A1
WO2023286171A1 PCT/JP2021/026335 JP2021026335W WO2023286171A1 WO 2023286171 A1 WO2023286171 A1 WO 2023286171A1 JP 2021026335 W JP2021026335 W JP 2021026335W WO 2023286171 A1 WO2023286171 A1 WO 2023286171A1
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Prior art keywords
search
data
input
optimization problem
information
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PCT/JP2021/026335
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English (en)
Japanese (ja)
Inventor
力 江藤
英恵 下村
大 窪田
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日本電気株式会社
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Priority to US18/575,386 priority Critical patent/US20240320216A1/en
Priority to JP2023534487A priority patent/JPWO2023286171A1/ja
Priority to PCT/JP2021/026335 priority patent/WO2023286171A1/fr
Publication of WO2023286171A1 publication Critical patent/WO2023286171A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G06F16/24534—Query rewriting; Transformation
    • G06F16/24542—Plan optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying

Definitions

  • the present invention relates to a search device, search method, and search program for searching registered information.
  • Patent Literature 1 describes a method of selecting one or more machine learning models from a plurality of machine learning models stored in advance in a database in response to a usage request obtained from a user-side device and providing the selected machine-learning models to the user-side device. It is In the method described in Patent Document 1, test data is applied to each of a plurality of machine learning models stored in a database, the performance of each model is calculated, and based on the calculated performance, provided to the user-side device. Choose a machine learning model to use.
  • the machine learning model described in Patent Document 1 assumes a prediction model generated by machine learning based on learning data and correct answer data, but it is assumed to be used for decision making such as combinatorial optimization problems. not.
  • an object of the present invention is to provide a search device, search method, and search program capable of searching for a desired optimization problem.
  • a search device selects from a database storing search information in which first data representing an optimization problem including an objective function and constraints and second data representing characteristics of the optimization problem are associated with each other.
  • search means for searching for optimization problems that match the search conditions; and input means for receiving input of second data as the search conditions, wherein the search means corresponds to search information that matches the input second data. It is characterized by retrieving the attached first data.
  • a computer receives an input of optimization problem characteristics as a search condition, and a computer receives first data representing an optimization problem including an objective function and constraints, and the optimization problem characteristics.
  • the optimization problem associated with the search information that matches the characteristics of the input optimization problem is searched from a database that stores search information associated with the second data indicating .
  • a search program provides a computer with a database storing search information in which first data representing an optimization problem including an objective function and constraints and second data representing characteristics of the optimization problem are associated with each other. , a search process for searching for optimization problems that match the specified search conditions, and an input process for accepting the input of second data as search conditions, and the search process for matching the input second data.
  • the feature is that the first data associated with the search information is searched.
  • a desired optimization problem can be searched.
  • FIG. 1 is a block diagram showing a configuration example of an embodiment of an information distribution system according to the invention
  • FIG. 4 is an explanatory diagram showing an example of types of decision making
  • It is a block diagram which shows the structural example of an information generation apparatus.
  • FIG. 4 is an explanatory diagram showing an example of search information;
  • It is a block diagram which shows the structural example of a search device.
  • It is a flow chart which shows an example of operation of an information generating device.
  • 4 is a flowchart showing an operation example of the search device 200
  • 1 is a block diagram showing a configuration example of an embodiment of a robot control system
  • FIG. 1 is a block diagram showing an overview of a search device according to the present invention
  • FIG. 1 is a schematic block diagram showing a configuration of a computer according to at least one embodiment;
  • FIG. 1 is a block diagram showing a configuration example of an embodiment of an information distribution system according to the present invention.
  • the information distribution system 1 of this embodiment includes an information generation device 100 , a search device 200 and a storage server 300 .
  • the information generation device 100, search device 200, and storage server 300 are interconnected through a communication line.
  • the information generation device 100 is a device that generates information (hereinafter referred to as search information) that is used by the search device 200, which will be described later, to search for optimization problems.
  • the optimization problem in this embodiment includes an objective function (more specifically, the structure of the objective function) and constraints formulated for the problem to be solved. Therefore, it can be said that the optimization problem in this embodiment represents the type of user's decision-making for the problem to be solved. A method of generating search information will be described later.
  • FIG. 2 is an explanatory diagram showing examples of types of decision making (that is, optimization problems).
  • the type of problem to be solved is classified as a so-called "shift scheduling problem", and the optimization problem is a violation degree ( explanatory variables), and two constraints (Conditions 4 and 5).
  • ⁇ of the objective function illustrated in FIG. 2 is a value indicating the degree to which the user attaches importance to the condition (hereinafter, may be referred to as the user's intention). set to the value By searching for a combination that minimizes the value of this objective function, it becomes possible to derive an appropriate action.
  • the storage server 300 stores the search information generated by the information generation device 100 .
  • the storage server 300 may manage search information using, for example, a general database (database system). Also, the storage server 300 may store search information generated by a device other than the information generation device 100 .
  • the search device 200 uses search information stored in the storage server 300 (more specifically, the database of the storage server 300) to search for optimization problems. Also, the search device 200 uses the searched optimization problem to derive the user's optimal behavior.
  • FIG. 3 is a block diagram showing a configuration example of the information generating device 100 of this embodiment.
  • the information generation device 100 of this embodiment includes a storage unit 110 , an input unit 120 , a feature generation unit 130 , a recommendation unit 140 , a learning unit 150 , a generation unit 160 and a registration unit 170 .
  • the storage unit 110 stores various information used when the information generating device 100 performs processing.
  • the storage unit 110 may store training data, parameters, learning results, and the like used for learning processing by the learning unit 150, which will be described later.
  • the storage unit 110 is realized by, for example, a magnetic disk or the like.
  • the input unit 120 accepts input of various information used when generating search information. Specifically, the input unit 120 receives information indicating an optimization problem including an objective function and constraints (hereinafter referred to as first data), information indicating characteristics of the optimization problem (hereinafter referred to as second data ) is accepted.
  • first data information indicating an optimization problem including an objective function and constraints
  • second data information indicating characteristics of the optimization problem
  • the input unit 120 may accept input of the first data and the second data from the user.
  • the input unit 120 may also receive an input of an objective function generated by the learning unit 150 (more specifically, an objective function stored in the storage unit 110).
  • the input unit 120 may also receive an input of second data (that is, information indicating the characteristics of the optimization problem) from the feature generation unit 130, which will be described later.
  • the input unit 120 may receive input of an optimization solver (or information specifying an optimization solver) that is a candidate for solving the optimization problem together with the first data.
  • optimization solvers include, for example, mathematical programming solvers.
  • a user who inputs an optimization problem often knows an optimization solver suitable for solving that optimization problem. Therefore, by accepting input of such information together with the first data, a user who searches for an optimization problem using a search device 200, which will be described later, can grasp the optimization solver to be used for the optimization problem. Become.
  • the first data represents information indicating the optimization problem by the structure of the objective function as described above, conditions indicating constraints, and the like.
  • the objective function may take any form, and may be, for example, a function represented by a linear sum of explanatory variables (conditions), as exemplified in FIG.
  • the form of the constraint is also arbitrary, and it may be information that specifies whether the constraint is satisfied or not satisfied by binary values, or information that indicates the degree to which the constraint is satisfied. .
  • the second data is arbitrary as long as it shows the characteristics of the optimization problem.
  • the second data need not be information that can uniquely identify the optimization problem.
  • Optimization problem characteristics include, for example, the type of optimization problem, attribute information such as included explanatory variables (feature values), when, where, who, and what kind of optimization decisions are made.
  • the optimization status indicating whether or not there is a problem, and usage conditions such as the range of available (disclosed) target users and date and time.
  • the type of optimization problem may be a template type of optimization problem, such as a schedule optimization problem or a knapsack problem, and the type of industry used (e.g., retail, manufacturing, travel, etc.) and so on.
  • the feature generation unit 130 generates features of the optimization problem. Specifically, the feature generation unit 130 generates the above-described second data from the first data. Any method may be used by the feature generator 130 to generate the second data. The feature generator 130 may, for example, automatically generate the second data based on a predetermined method, or may generate the second data based on a user's designation.
  • the feature generation unit 130 determines the optimization problem according to the weight (that is, the degree of importance) of the explanatory variables included in the objective function.
  • Features may be generated.
  • the feature generation unit 130 may generate, for example, the content of the explanatory variable with the highest weight as the feature of the optimization problem.
  • the feature generation unit 130 optimizes the contents of the specified explanatory variables. It may be generated as a feature of the problem.
  • the feature generation unit 130 may also identify the type of optimization problem based on explanatory variables (feature amounts) included in the objective function, and generate the identified optimization problem type as a feature. For example, assume that the input unit 120 has received an input of an objective function including an explanatory variable "employee's working hours". In this case, it can be said that the type of optimization problem is more likely to be "shift scheduling" than "order quantity optimization". This is because there is a high possibility that "employee's working hours" is included in "shift scheduling” as a feature amount. Therefore, the type of optimization problem corresponding to the explanatory variable is determined in advance, and the feature generation unit 130 generates the corresponding predetermined type of optimization problem as a feature based on the explanatory variable included in the objective function. You may
  • the recommendation unit 140 recommends the features generated by the feature generation unit 130 to the user. Specifically, the recommendation unit 140 presents the recommended features to the user and causes the user to designate them. Note that the recommendation unit 140 may present features in any manner, and may present content such as the second data illustrated in FIG. 4, for example. Also, the number of features that the recommendation unit 140 causes the user to specify is not limited, and may be one or more.
  • the learning unit 150 generates an objective function by machine learning using the training data stored in the storage unit 110. Specifically, the learning unit 150 performs optimization problems (more specifically, to generate the objective function of the first data).
  • the learning unit 150 may receive an input of a prediction model that derives a desired prediction result. Then, the learning unit 150 may generate an objective function using the prediction result of the received prediction model as an explanatory variable. Examples of future prediction results include product demand prediction, road congestion prediction, and visitor number prediction. Note that when the prediction model is stored in advance in the storage unit 110 , the learning unit 150 may acquire the prediction model stored in the storage unit 110 .
  • the feature generation unit 130 may generate, as the second data, information indicating the user who generated the training data used for learning the objective function. By using such information, it becomes possible to use the information of the user who made the decision on which the objective function is generated as a feature of the optimization problem.
  • the learning unit 150 stores the generated objective function in the storage unit 110. If the optimization problem (more specifically, the objective function) has already been generated or if there is no need to learn the objective function, the information generating device 100 may not include the learning unit 150. .
  • the generation unit 160 generates search information in which the first data and the second data are associated. As a result, information for retrieving the optimization problem can be generated, so that the first data associated with the second data can be retrieved using the second data as a key.
  • the generation unit 160 may generate search information including information on the optimization solver. This makes it possible to know which optimization solvers are available for the corresponding optimization problem.
  • the generation unit 160 may generate search information in which the feature specified by the user is included in the second data.
  • the registration unit 170 registers the generated search information in the storage server 300.
  • FIG. 4 is an explanatory diagram showing an example of search information stored in the storage server 300.
  • FIG. FIG. 4 illustrates search information in which the first data and the second data are associated with each other.
  • the search information in the first row indicates that the optimization problem includes an objective function represented by a linear sum of three conditions (explanatory variables) and two constraints to be satisfied.
  • the optimization problem corresponds to the type of so-called "schedule optimization" problem, and while emphasizing time efficiency, the purpose is to emphasize actions such as spending time slowly at night. It indicates that
  • the optimization problem shown in the first line relates to the situation when a man in his twenties traveled to Osaka, and indicates that all users can use it for one month. Furthermore, it indicates that “solver A” is specified as an optimization solver candidate for solving the optimization problem shown in the first line.
  • the first data does not need to be associated with all of the assumed features as illustrated in FIG. 4, and only some of the features may be associated.
  • the classification and representation of the features included in the second data are examples. These features may be represented, for example, based on index specifications defined in each database.
  • the input unit 120, the feature generation unit 130, the recommendation unit 140, the learning unit 150, the generation unit 160, and the registration unit 170 are computer processors (e.g., CPU (Central Processing Unit) and GPU (Graphics Processing Unit)).
  • CPU Central Processing Unit
  • GPU Graphics Processing Unit
  • the program is stored in the storage unit 110 included in the information generation device 100, the processor reads the program, and according to the program, the input unit 120, the feature generation unit 130, the recommendation unit 140, the learning unit 150, the generation unit 160, Also, it may operate as the registration unit 170 . Also, the functions of the information generating device 100 may be provided in a SaaS (Software as a Service) format.
  • SaaS Software as a Service
  • the input unit 120, the feature generation unit 130, the recommendation unit 140, the learning unit 150, the generation unit 160, and the registration unit 170 may each be realized by dedicated hardware. Also, part or all of each component of each device may be implemented by general-purpose or dedicated circuitry, processors, etc., or combinations thereof. These may be composed of a single chip, or may be composed of multiple chips connected via a bus. A part or all of each component of each device may be implemented by a combination of the above-described circuits and the like and programs.
  • each component of the information generating device 100 is realized by a plurality of information processing devices, circuits, etc.
  • the plurality of information processing devices, circuits, etc. may be centrally arranged, They may be distributed.
  • the information processing device, circuits, and the like may be implemented as a form in which each is connected via a communication network, such as a client-server system, a cloud computing system, or the like.
  • FIG. 5 is a block diagram showing a configuration example of the search device 200 of this embodiment.
  • the search device 200 of this embodiment includes a search condition input unit 210, a search unit 220, a model input unit 230, a problem extraction unit 240, a problem generation unit 250, an optimization unit 260, and an output unit 270. include.
  • the search condition input unit 210 accepts input of conditions for searching for optimization problems (hereinafter sometimes simply referred to as search conditions). Specifically, the search condition input unit 210 receives input of information (specifically, second data) indicating characteristics of the optimization problem as a search condition.
  • the form of information to be input is arbitrary, and the search condition input unit 210 may extract search conditions based on assumed input.
  • the search condition input unit 210 may, for example, accept input of a character string indicating the content to be solved as the optimization problem.
  • the search condition input unit 210 may extract features of the optimization problem to be extracted from the input character string based on known natural language processing. The extracted features are used by the search unit 220, which will be described later.
  • the search condition input unit 210 selects "male in his twenties", A feature such as "travel to Osaka” may be extracted.
  • the search condition input unit 210 may accept input of information indicating the optimization problem (specifically, information indicating the first data) as the search condition.
  • the search condition input unit 210 may, for example, display a list of candidates for the first data representing the optimization problem and receive designation of the candidates from the user.
  • the search unit 220 receives search conditions specified by input to the search condition input unit 210 from the storage server 300 (more specifically, a database that stores search information in which the first data and the second data are associated). Find optimization problems that match . More specifically, the search unit 220 searches for first data (that is, optimization problem) associated with search information that matches input second data (that is, optimization problem characteristics).
  • the search unit 220 may search for optimization problems that match the input information indicating the first data.
  • the search unit 220 may also search for the corresponding optimization solver.
  • the search unit 220 may present the search results to the user and accept the specification of the optimization problem desired by the user.
  • the search unit 220 may display the characteristics of the optimization problem (that is, the second data) together with the first data as the search result, and accept the user's designation of the displayed first data.
  • the model input unit 230 accepts input of a model (hereinafter also referred to as a decision-making model) learned based on the target user's decision-making history data. That is, the model input unit 230 receives an input of a model that reflects the target user's past decision-making. Note that the model input unit 230 may also receive input of constraints imposed on the model together with the model.
  • a model hereinafter also referred to as a decision-making model
  • the method by which the model that accepts input is generated is arbitrary.
  • the model input unit 230 may receive an input of a model learned by a method (for example, inverse reinforcement learning) similar to the method by which the learning unit 150 of the information generating device 100 generates the objective function.
  • the received model is compared with the optimization problem retrieved by the retrieval unit 220. Therefore, the input model is preferably a model generated by a method similar to the optimization problem to be retrieved, or a model generated in a similar manner.
  • the problem extraction unit 240 compares the optimization problem retrieved by the retrieval unit 220 with the decision-making model whose input is received by the model input unit 230, and determines the retrieved optimization problem (i.e., first data). Among them, an optimization problem that satisfies a predetermined condition in terms of similarity to the input decision-making model is extracted. Predetermined conditions include, for example, that the degree of similarity is greater than a predetermined threshold.
  • the problem extraction unit 240 can extract an optimization problem reflecting the intention of being similar to the target user by judging the degree of similarity between such a model and the optimization problem.
  • the method by which the problem extraction unit 240 calculates the degree of similarity is arbitrary.
  • the problem extraction unit 240 may calculate the similarity between feature quantities (explanatory variables) included in each of the decision-making model and the optimization problem.
  • the problem extraction unit 240 may calculate, for example, the degree of overlapping of types of feature amounts as the degree of similarity.
  • the problem extraction unit 240 may calculate the similarity by calculating the difference in the value of the weighting factor of each overlapping feature quantity, for example, by cosine similarity or RMSE (Root Mean Square Error). .
  • RMSE Root Mean Square Error
  • the problem extracting unit 240 extracts the degree of overlap of the range of values that can be taken by the same feature quantity, the average of each feature quantity Alternatively, variance may be calculated as the degree of similarity.
  • the method by which the problem extraction unit 240 calculates the degree of similarity is not limited to the method based on the structure of the decision-making model and the optimization problem as described above.
  • the problem extraction unit 240 may calculate the degree of similarity based on the difference in output values when the same data is input to each of the decision-making model and the optimization problem.
  • the problem extraction unit 240 calculates the similarity between the sentences. It may be calculated as degrees.
  • the search device 200 does not necessarily need to accept the input of the decision-making model. If there is no decision-making model input, the search device 200 may not include the model input unit 230 and the problem extraction unit 240 .
  • the problem generation unit 250 When a plurality of optimization problems are extracted, the problem generation unit 250 generates a new objective function from the plurality of extracted optimization problems. Specifically, the problem generator 250 combines two or more objective functions selected from a plurality of optimization problems to generate a new objective function. Note that when one optimization problem is specified, the problem generator 250 does not need to generate a new objective function.
  • combining two or more objective functions means extracting some or all of the explanatory variables included in each objective function based on a predetermined rule, and using the extracted explanatory variables to formulate (function It means to convert).
  • this rule is determined by a user or the like according to the degree to which the intention indicated by each objective function is reflected.
  • a predetermined rule for example, there is a method of multiplying the weight of the original explanatory variables by a predetermined ratio, extracting all the explanatory variables, and then calculating the sum of the respective extracted explanatory variables.
  • objective function A and objective function B include a plurality of overlapping features f 1 , f 2 , and f 3 as illustrated below.
  • Objective function A a1 *f1 + a2 *f2+ a3 * f3
  • Objective function B b1 *f1 + b2 *f2 + b3 * f3
  • the problem generator 250 adds the objective functions obtained by multiplying the coefficients of the explanatory variables by 0.5 so that the combination ratio is 1:1, and obtains the objective function C may be generated.
  • Objective function C 0.5* ( a1+ b1 )*f1 + 0.5* ( a2 + b2)*f2+0.5*( a3 + b3 )*f3
  • the question generator 250 may combine the objective variables so that the combination ratio is, for example, 4:1 to generate an objective function D as exemplified below.
  • Objective function D (0.8*a1+0.2* b1 )* f1 + (0.8* a2 + 0.2* b2 )*f2+(0.8* a3 +0.2 * b3 ) * f3
  • the optimization unit 260 performs optimization processing based on the generated new objective function and derives the optimization result. Further, for example, when an optimization solver candidate is designated, the optimization unit 260 may derive the optimization result using the designated optimization solver.
  • the output unit 270 outputs the optimization result (eg, optimal behavior) derived by the optimization unit 260.
  • the search condition input unit 210, search unit 220, model input unit 230, problem extraction unit 240, problem generation unit 250, optimization unit 260, and output unit 270 operate according to a program (search program). Implemented by a computer processor.
  • the program is stored in a storage unit (not shown) provided in the search device 200, the processor reads the program, and according to the program, the search condition input unit 210, the search unit 220, the model input unit 230, the problem extraction unit 240 , problem generator 250 , optimizer 260 and output unit 270 .
  • the functions of the search device 200 may be provided in a SaaS (Software as a Service) format.
  • a search condition input unit 210 may be implemented by dedicated hardware.
  • a search unit 220 may be implemented by dedicated hardware.
  • a model input unit 230 may be implemented by dedicated hardware.
  • a problem extraction unit 240 may be implemented by dedicated hardware.
  • a problem generation unit 250 may be implemented by dedicated hardware.
  • an optimization unit 260 may be implemented by dedicated hardware.
  • FIG. 6 is a flowchart showing an operation example of the information generation device 100 of this embodiment.
  • the input unit 120 receives inputs of first data representing an optimization problem including an objective function and constraints, and second data representing features of the optimization problem (step S11).
  • the generation unit 160 generates search information in which the first data and the second data are associated (step S12).
  • the registration unit 170 registers the generated search information in the storage server 300 (step S13).
  • FIG. 7 is a flowchart showing an operation example of the search device 200 of this embodiment.
  • the search condition input unit 210 receives input of information (that is, second data) indicating the characteristics of the optimization problem (step S21).
  • the search unit 220 searches the storage server 300 for optimization problems (that is, first data) associated with search information that matches the input information (that is, second data) (step S22). Thereafter, the optimization unit 260 performs optimization processing using the retrieved first data, and the output unit 270 outputs the optimization result.
  • the input unit 120 receives inputs of the first data and the second data
  • the generation unit 160 generates search information in which the first data and the second data are associated with each other. . Therefore, information for searching optimization problems can be generated.
  • optimization problems are generally generated individually in consideration of the user's point of view and various constraints. There was no technical idea to On the other hand, in the present embodiment, since the generation unit 160 generates search information in which the first data and the second data are associated, it is possible to manage the optimization problem using this search information.
  • the search condition input unit 210 receives input of second data as a search condition
  • the search unit 220 receives the first data associated with the search information that matches the input second data. is retrieved from the storage server 300 . Therefore, a desired optimization problem can be retrieved.
  • the search unit 220 searches the storage server 300 for the first data associated with the search information that matches the input second data. Therefore, it becomes possible to obtain a desired optimization problem.
  • FIG. 8 is a block diagram showing a configuration example of an embodiment of the robot control system.
  • a robot control system 2000 illustrated in FIG. 8 includes a search device 200 and a robot 2300 .
  • the search device 200 illustrated in FIG. 8 is the same as the search device 200 in the above embodiment.
  • the search device 200 stores the optimization result in the storage unit 2310 of the robot 2300, which will be described later.
  • the robot 2300 is a device that operates based on the optimization results. It should be noted that the robots here are not limited to devices shaped like humans or animals, and include devices that perform automatic work (automatic operation, automatic control, etc.). Robot 2300 includes a storage unit 2310 , an input unit 2320 and a control unit 2330 .
  • the storage unit 2310 stores the optimization results derived by the search device 200.
  • the input unit 2320 accepts input of various information used when operating the robot.
  • the control unit 2330 controls the motion of the robot 2300 based on the received various information and the optimization results stored in the storage unit 2310. Note that a method for controlling the operation of the robot 2300 by the control unit 2330 based on the optimization result may be determined in advance. In this embodiment, a device that performs automatic work, such as the robot 2300, can be controlled based on the derived optimization results.
  • FIG. 9 is a block diagram showing an overview of a search device according to the present invention.
  • a search device 90 for example, a search device 200
  • a search means 91 for example, a search unit 220
  • searches for an optimization problem that matches a specified search condition from a database for example, a storage server 300
  • a database for example, a storage server 300
  • input means 92 for example, the search condition input unit 210.
  • the search means 91 searches for the first data associated with the search information matching the input second data.
  • the input means 92 may receive input of information indicating the first data, and the search means 91 may search for optimization problems that match the input information indicating the first data.
  • the search device 90 includes model input means (for example, the model input unit 230) that receives an input of a decision-making model that is a model learned based on the user's decision-making history data, and and problem extracting means (for example, problem extracting section 240) for extracting first data whose degree of similarity to the input decision-making model satisfies a predetermined condition.
  • model input means for example, the model input unit 230
  • problem extracting means for example, problem extracting section 240
  • the search device 90 may include problem generating means (for example, the problem generating unit 250) that generates a new objective function by combining two or more objective functions selected from a plurality of first data. Then, the searching means 91 may search for a plurality of first data.
  • problem generating means for example, the problem generating unit 250
  • the searching means 91 may search for a plurality of first data.
  • the search device 90 may also include optimization means for optimizing the objective function.
  • the input means 92 receives an input of a character string indicating the content to be solved as an optimization problem, extracts the characteristics of the optimization problem from the received character string, and the search means 91 performs a search matching the extracted characteristics. You may search the 1st data matched with information.
  • FIG. 10 is a schematic block diagram showing the configuration of a computer according to at least one embodiment.
  • a computer 1000 comprises a processor 1001 , a main storage device 1002 , an auxiliary storage device 1003 and an interface 1004 .
  • Each device (the information generation device 100 and the search device 200) of the information distribution system 1 described above is implemented in the computer 1000, respectively.
  • the operation of each processing unit described above is stored in the auxiliary storage device 1003 in the form of a program.
  • the processor 1001 reads out the program from the auxiliary storage device 1003, develops it in the main storage device 1002, and executes the above processing according to the program.
  • the secondary storage device 1003 is an example of a non-transitory tangible medium.
  • Other examples of non-transitory tangible media include magnetic disks, magneto-optical disks, CD-ROMs (Compact Disc Read-only memory), DVD-ROMs (Read-only memory), connected via interface 1004, A semiconductor memory etc. are mentioned.
  • the computer 1000 receiving the distribution may develop the program in the main storage device 1002 and execute the above process.
  • the program may be for realizing part of the functions described above.
  • the program may be a so-called difference file (difference program) that implements the above-described functions in combination with another program already stored in the auxiliary storage device 1003 .
  • the input means receives input of information indicating the first data,
  • the search device according to appendix 1, wherein the search means searches for an optimization problem that matches the input information indicating the first data.
  • the search device according to appendix 1 or appendix 2, further comprising problem extracting means for extracting, from among retrieved first data, first data whose degree of similarity to the input decision-making model satisfies a predetermined condition.
  • Appendix 4 Combining two or more objective functions selected from a plurality of first data to generate a new objective function,
  • the search device according to any one of Appendixes 1 to 3, wherein the search means searches for a plurality of first data.
  • Supplementary note 5 The search device according to any one of Supplementary notes 1 to 4, including an optimization means for performing optimization processing of an objective function.
  • the input means receives input of a character string indicating the content to be solved as the optimization problem, extracts features of the optimization problem from the received character string,
  • the search device according to any one of appendices 1 to 5, wherein the search means searches for first data associated with search information that matches the extracted feature.
  • the computer receives input of optimization problem features as search conditions, The computer performs optimization input from a database storing search information in which first data representing an optimization problem including an objective function and constraints and second data representing characteristics of the optimization problem are associated with each other.
  • a retrieval method characterized by retrieving an optimization problem associated with retrieval information that matches the features of the problem.
  • Appendix 8 The computer receives input of information indicating the first data, The search method according to appendix 7, wherein the computer searches for an optimization problem that matches the input information indicating the first data.
  • Appendix 10 to the computer, receiving input of information indicating the first data in the input process;
  • Appendix 12 to the computer, receiving input of information indicating the first data in the input process; 12.
  • Information distribution system 100 information generation device 110 storage unit 120 input unit 130 feature generation unit 140 recommendation unit 150 learning unit 160 generation unit 170 registration unit 200 search device 210 search condition input unit 220 search unit 230 model input unit 240 problem extraction unit 250 Problem generator 260 Optimization unit 270 Output unit 300 Storage server

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  • Engineering & Computer Science (AREA)
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  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Operations Research (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne un moyen de recherche 91 qui recherche un problème d'optimisation qui correspond à une condition de recherche spécifiée à partir d'une base de données qui stocke des informations de recherche dans lesquelles des premières données indiquant un problème d'optimisation qui comprend une fonction objective et une contrainte et des secondes données indiquant une caractéristique du problème d'optimisation sont associées les unes aux autres. Un moyen d'entrée 92 reçoit une entrée des secondes données en tant que condition de recherche.
PCT/JP2021/026335 2021-07-13 2021-07-13 Dispositif de recherche, procédé de recherche et programme de recherche WO2023286171A1 (fr)

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US18/575,386 US20240320216A1 (en) 2021-07-13 2021-07-13 Search device, search method, and search program
JP2023534487A JPWO2023286171A1 (fr) 2021-07-13 2021-07-13
PCT/JP2021/026335 WO2023286171A1 (fr) 2021-07-13 2021-07-13 Dispositif de recherche, procédé de recherche et programme de recherche

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7424451B1 (en) * 2000-10-10 2008-09-09 International Business Machines Corporation System and method of solving optimization problems using prestored advanced bases

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7424451B1 (en) * 2000-10-10 2008-09-09 International Business Machines Corporation System and method of solving optimization problems using prestored advanced bases

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ETO, CHIKARA ET AL.: "Intentional learning technology that imitates expert decision making", NEC TECHNICAL JOURNAL, vol. 72, no. 1, 31 October 2019 (2019-10-31), pages 95 - 98, XP055910107 *

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